Development of a methodology for plausibility checks for linear structural mechanic finite element simulations using Deep Learning

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In the current industrial environment, design accompanying linear finite element simulations are often carried out by product developers and not exclusively by calculation engineers with several years of professional experience. This leads to frequent iterations in the product development process and can lead to incorrect decisions based on insufficiently validated results. An automatic plausibility check for linear structural-mechanical FE simulations is an important method to support product developers. The use of Convolutional Neural Networks (CNN) and Machine Learning methods represents an enormous potential to identify correlations in data and to build a model with high prediction quality. In the applicant's preparatory work it could be shown that a plausibility check for FE calculations using Deep Learning and CNNs is possible. However, it is necessary to increase the prediction quality of the artificial neural network by adjusting the parameters and to demonstrate the application of the method to new simulations. Furthermore, local areas of the FE-simulation shall be investigated, especially to detect numerical errors like singularities.The aim of the project is to create a method for plausibility checks of similar linear structural-mechanical FE simulations based on the preliminary work on the projection method and singularity recognition. Furthermore, the network parameters of different CNNs and machine learning methods are to be optimized in order to implement a plausibility check with high prediction quality. FE-simulation results cannot be directly transferred to a neural network or machine learning algorithm, but have to be converted to a uniform computer-processable form. Within the framework of the research project, the projection method developed in preliminary work is applied, which uses spherical detector surfaces to transform arbitrary simulations into matrices with uniform size. The generated matrices contain all relevant information to classify a simulation as plausible or implausible. A Deep Learning CNN or SVM does the classification. In addition to the classification of the entire simulation, local areas should also be examined. Especially singularities in FE-simulations shall be detected and accordingly give feedback to the user.With an automatic plausibility check, errors in the simulation setup can be detected automatically at an early stage. It therefore represents an enormous potential for increasing the simulation quality in virtual product development. Especially if FE simulations are performed by product developers who have less simulation knowledge than experienced calculation engineers. A method will be developed which allows to consider similar geometries and simulation boundary conditions of linear FE-simulations.